Applied RL Engineer

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an Applied RL Engineer for a fully remote US-based role. You will design, train, and deploy RL systems for high-impact decision-making, translating research into robust production solutions.

The candidate should have 6+ years of RL experience, strong Python and DL framework skills, and experience with scalable RL libraries and GPU clusters. Excellent collaboration and communication are essential for cross-functional teams.

Qualifications

• Master’s or PhD in Computer Science, ML, or related field (or equivalent applied experience).
• Six+ years of RL research and engineering experience.
• Strong proficiency in Python and modern deep learning frameworks.
• Hands-on experience with at least one major RL library or in-house RL stack.
• Solid understanding of probability, optimization, and RL foundations.

Responsibilities

• Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
• Develop, calibrate, and maintain simulation environments for large-scale agent training.
• Implement and evaluate RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
• Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
• Apply offline RL and imitation learning techniques where exploration is costly or unsafe.
• Use RLHF, DPO, and related techniques for fine-tuning LLMs when relevant.
• Build scalable training infrastructure for distributed RL, including experience collection and replay systems.
• Optimize training stability and sample efficiency through algorithmic improvements.
• Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases.
• Implement safety mechanisms such as constraint enforcement and human-in-the-loop oversight.
• Collaborate with applied scientists and product teams to identify high-value RL use cases.
• Monitor deployed policies and models for drift and unintended behaviors; build alerts and dashboards.
• Document methodology and operational characteristics for internal stakeholders.
• Stay current with RL research and translate techniques into production-ready solutions.

Skills

Python proficiency

Deep learning frameworks

RL library experience

GPU cluster training

Strong communication

Research to production

Probability & optimization

Education

Master’s or PhD in Computer Science, ML or related field

Tools

PyTorch

TensorFlow

RL libraries

Job description

Applied RL Engineer-Remote

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title:

Applied RL Engineer

Location:

100% Remote (United States)

Position Type:

Full-time, Direct W2

Salary Range:

$100,000–$150,000 Annually

Experience:

6+ years

Sponsorship:

U.S. Citizens, Green CardHolders, EADHolders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are looking for a Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale. The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical.

Key Responsibilities

• Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
• Develop, calibrate, and maintain simulation environments suitable for large-scale agent training.
• Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
• Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
• Apply offline RL and imitation learning techniques where exploration is costly or unsafe.
• Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant.
• Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems.
• Optimize training stability and sample efficiency through algorithmic and engineering improvements.
• Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases.
• Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight.
• Collaborate with applied scientists and product teams to identify high-value RL use cases.
• Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users.
• Document methodology, design decisions, and operational characteristics for internal stakeholders.
• Stay current with RL research and translate promising techniques into production-ready solutions.

Required Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience.
• Six or more years of combined RL research and engineering experience.
• Strong proficiency in Python and modern deep learning frameworks.
• Hands-on experience with at least one major RL library or in-house RL stack.
• Solid understanding of probability, optimization, and the theoretical foundations of RL.
• Experience designing and tuning reward functions in non-trivial environments.
• Familiarity with simulation environments and large-scale experience collection.
• Experience training neural network policies on GPU clusters.
• Strong written and verbal communication skills.
• Track record of shipping or publishing impactful RL work.

Preferred Qualifications

• Experience with RLHF for large language models.
• Familiarity with multi-agent RL or hierarchical RL.
• Exposure to robotics, control systems, or autonomous driving.
• Publications in RL or related research venues.
• Open-source contributions to RL libraries or environments.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.